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MLOps Internship

Take machine learning models from notebooks to production: versioning, experiment tracking, model APIs, CI, monitoring and retraining.

PythonMLflowDVCFastAPIDockerEvidently

What you'll work on

Full syllabus: all 60 graded tasks

These are the actual tasks the internship portal assigns for this domain. You submit each one with proof (a GitHub link or document) and a mentor reviews it.

Level 1: Foundations (24 tasks)
  1. What MLOps is. Explain the ML lifecycle (data, training, deployment, monitoring) and where MLOps fits, with a diagram.
  2. Reproducible environment. Create a project with a pinned requirements file or conda environment and a README that anyone can follow.
  3. Train a baseline model. Train a simple scikit-learn model on a public dataset and record its accuracy and training time.
  4. Project structure. Organise your ML code into data, features, train and predict modules instead of one notebook.
  5. Version code with Git. Commit your project with a clear history and a .gitignore that keeps data and models out of Git.
  6. Version data with DVC. Track your dataset with DVC and show how to switch between two versions of the data.
  7. Experiment tracking. Log parameters, metrics and the model for 5 training runs with MLflow and compare them.
  8. Save and load models. Save a trained model with joblib or ONNX and load it in a separate script to predict.
  9. Model serving API. Serve your model with FastAPI or Flask: POST features, get a prediction back as JSON.
  10. Input validation. Validate prediction requests with Pydantic and return clear errors for bad input.
  11. Unit tests for ML code. Write tests for data cleaning, feature code and the prediction endpoint.
  12. Containerise the model. Write a Dockerfile for the model API and run it locally.
  13. Makefile or task runner. Add make (or invoke/just) commands for setup, train, test and serve.
  14. Config files for training. Move hyperparameters to a YAML config and load it with Hydra or a simple loader.
  15. Random seeds. Make training reproducible with fixed seeds and show two identical runs.
  16. Data schema. Define the expected input schema with Pandera or Great Expectations and validate it.
  17. Model card. Write a model card: purpose, data, metrics, limits and ethical considerations.
  18. Notebook to script. Convert an exploratory notebook into clean, tested Python modules.
  19. Pre-commit for ML. Add black, ruff and nbstripout as pre-commit hooks.
  20. Environment with Docker. Build a training image so anyone can run training with one command.
  21. Compare model formats. Save a model as pickle, joblib and ONNX and compare size and load time.
  22. Simple dashboard of runs. Show your MLflow runs in a table and pick the best model with a rule.
  23. API contract. Document your prediction API with OpenAPI, including examples.
  24. Cost of training. Estimate the cost of training your model on a cloud GPU vs a CPU.
Level 2: Building real features (20 tasks)
  1. Training pipeline. Turn data preparation, training and evaluation into a pipeline (DVC pipeline, Prefect or Airflow).
  2. CI for ML. Add GitHub Actions that run tests and a quick training check on every push.
  3. Model registry. Register models in MLflow's registry and move one from staging to production.
  4. Deploy to the cloud. Deploy the model API to a free cloud service and share the live URL.
  5. Batch prediction. Build a batch job that scores a whole CSV file and writes the results with a timestamp.
  6. Feature store concept. Explain feature stores and build a simple shared feature table used by training and serving.
  7. Hyperparameter tuning. Tune your model with Optuna or GridSearch and log every trial to MLflow.
  8. Model evaluation report. Produce an evaluation report with metrics, confusion matrix and error analysis on slices of data.
  9. Load testing. Load-test your model API with Locust or k6 and report latency and throughput.
  10. Logging predictions. Log every prediction with inputs, output, model version and time for later monitoring.
  11. Feature pipeline. Separate feature computation into a reusable pipeline used by training and serving.
  12. Pipeline orchestration. Run your pipeline in Airflow, Prefect or Kubeflow Pipelines with retries.
  13. Model validation gate. Block promotion of a model that performs worse than the current one on a holdout set.
  14. Shadow deployment. Run a new model in shadow mode next to the old one and compare predictions.
  15. Serving with BentoML or Ray Serve. Serve your model with a model-serving framework and compare with FastAPI.
  16. GPU basics. Train a small deep learning model on a free GPU notebook and log GPU usage.
  17. Prediction caching. Cache predictions for repeated inputs and measure latency improvement.
  18. Secrets and credentials. Keep cloud and database credentials out of code in your ML pipeline.
  19. Data versioning workflow. Show a full workflow: change data, retrain, compare and roll back with DVC.
  20. Explainability. Add SHAP explanations to predictions and show them in the API response.
Level 3: Professional work and capstone (16 tasks)
  1. Data drift detection. Use Evidently or a custom check to compare new data with training data and flag drift.
  2. Model monitoring dashboard. Build a dashboard with prediction volume, latency and drift over time.
  3. Automated retraining. Trigger retraining when drift is detected or on a schedule, and register the new model only if it beats the old one.
  4. Kubernetes deployment. Deploy the model to a local Kubernetes cluster with a health check and two replicas.
  5. A/B or canary release. Route a share of traffic to a new model version and compare their results.
  6. LLM serving basics. Serve a small open model or an LLM API behind your own endpoint with caching and rate limits.
  7. Responsible ML checks. Check your model for bias across one sensitive attribute and document the result and fix.
  8. Capstone: end-to-end MLOps. Deliver a full MLOps project: versioned data, tracked experiments, CI, deployed API, monitoring and retraining, with an architecture diagram.
  9. Concept drift simulation. Simulate concept drift on a dataset and show your monitoring catching it.
  10. Feature store with Feast. Set up Feast with offline and online stores for one feature set.
  11. LLMOps basics. Version prompts, log LLM calls and evaluate outputs for an LLM feature.
  12. RAG evaluation pipeline. Build an automated evaluation for a RAG app with a fixed question set.
  13. Autoscaling model service. Configure autoscaling for your model service and test it under load.
  14. Governance and audit. Record who trained, approved and deployed each model version.
  15. Incident runbook for models. Write a runbook for a model producing bad predictions in production.
  16. Capstone 2: ML platform template. Build a reusable MLOps template repo (data checks, training, tracking, CI, serving, monitoring) and document how a team would use it.

Real software work, not just training

Every MLOps task is reviewed by our team and saved to your portfolio. Interns who pass the readiness check can take real work from Training Trains, W3AppDevelopers and DomainHostly projects on the Company Work board.

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Frequently asked questions

Do I need experience to join the MLOps internship?+

You should know Python and basic machine learning (training a scikit-learn model).

What will I do in the MLOps internship?+

You complete 60 graded tasks in three levels: foundations, real features, and professional work. Each task is submitted with GitHub or document proof and reviewed. The final capstone is: Capstone 2: ML platform template.

Do I get a certificate for the MLOps internship?+

Yes. After your required tasks are approved, your attendance is above 75% and you pass the proctored final exam, you get a graded completion certificate that anyone can verify at trainingtrains.com/verify.

Can I do the MLOps internship online?+

Yes. You can do it fully online from home, or offline at our Erode and Perundurai branches. Tasks, quizzes and mentor reviews are the same in both modes.

Available both online (work from home) and offline at our Erode and Perundurai branches - see the full internship program details for departments, duration, and the application process.

Apply for the MLOps internship